Daniel P. Playne

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Graph component labelling, which is a subset of the general graph colouring problem, is a computationally expensive operation that is of importance in many applications and simulations. A number of data-parallel algorithmic variations to the component labelling problem are possible and we explore their use with general purpose graphical processing units(More)
Graphical Processing Units (GPUs) have recently attracted attention for scientific applications such as particle simulations. This is partially driven by low commodity pricing of GPUs but also by recent toolkit and library developments that make them more accessible to scientific programmers. We report on two further application paradigms – regular mesh(More)
Many simulations in the physical sciences are expressed in terms of rectilinear arrays of variables. It is attractive to develop such simulations for use in 1-, 2-, 3or arbitrary physical dimensions and also in a manner that supports exploitation of data-parallelism on fast modern processing devices. We report on data layouts and transformation algorithms(More)
Lattice gas cellular automata (LGCA) models provide a relatively fast means of simulating fluid flow and can give both quantitative and qualitative insights into flow patterns around complex obstacles. Symmetry requirements inherent in the Navier-Stokes equation mandate that lattice-gas approximations to the full field equations be run on triangular(More)
Computational scientific simulations have long used parallel computers to increase their performance. Recently graphics cards have been utilised to provide this functionality. GPGPU APIs such as NVidia’s CUDA can be used to harness the power of GPUs for purposes other than computer graphics. GPUs are designed for processing two-dimensional data. In previous(More)
Data-parallel accelerator devices such as Graphical Processing Units (GPUs) are providing dramatic performance improvements over even multi-core CPUs for lattice-oriented applications in computational physics. Models such as the Ising and Potts models continue to play a role in investigating phase transitions on small-world and scale-free graph structures.(More)
Graphical Processing Units (GPUs) are good data-parallel performance accelerators for solving regular mesh partial differential equations (PDEs) whereby low-latency communications and high compute to communications ratios can yield very high levels of computational efficiency. Finite-difference time-domain methods still play an important role for many PDE(More)